Data Warehousing, Data Mining, and Their Applications

2462 Words5 Pages

Abstract: This paper covers trends in the data mining and data warehousing industry. It covers applications and new possibilities in the field along with risks involved, limitations, and possible questions surrounding ethical usage of information.

As computing power has increased over the past few decades, the industry has found many innovative solutions to previously impossible problems. The raw increase in computing power, and the ability to push numbers and move large amounts of data in reasonable amounts of time have enhanced the abilities and sizes of databases. Companies such as NCR, can now maintain databases of sizes greater than a terrabyte. (For which 1 Terrabyte = 1024 Gigabytes, 1 Gigabyte = 1024 Megabytes, 1 Megabyte = 1024 bytes. 1 Terrabyte is 1,099,511,627,776 bytes.) Of course size is irrelevant to database unless there is a fast mining time, or a quick response time to data queries to the database. Oracle, Informix, and NCR are some database mining companies that provide quick response to data queries on their databases and provide a new world of opportunities. Data mining today can allow for companies to create customer profiles, manipulate information easily, and provide knowledgeable access to the current state of a their company. However, a reality that many companies find out the hard way, is that data mining and data warehousing does not work for them. As with many new tools or technology, companies may jump on the bandwagon without contemplating its potential weaknesses. In order to remain competitive in today’s business world, companies should consider implementing data warehouses, but only with adequate research taking into account the benefits and weaknesses of such an initiative.

Data mining has created new ways of moving information around and has allowed for novel applications in using information. Any range of companies will accrue vast amounts of data and statistics during their normal operations. Data includes vital information such as sales, overhead, distribution, and chain locations, to information such as customer purchases, sales demographics, and rate of sales per store location. The sheer amount of information generated by even modestly small companies can be staggering. Prior to reliable data warehousing solutions, most of the information was discarded as there was no reasonable way to make use of the information. However, Han, Urban and Dasgupta point out that data mining can be used to find relationships in data that might seem unrelated.

More about Data Warehousing, Data Mining, and Their Applications

Open Document